Tomography refers to imaging by sectioning or sections using any kind of penetrating signal or wave. A device used in tomography is called a tomograph, while the image produced is a tomogram. In most cases tomography is based upon the mathematical procedure known as tomographic reconstruction. Initially developed between the 1960s and 1970s, the Computed Tomographic (CT) scanner made exceptional contribution to medicine, which was the first application of the process. In recent years, tomography has spread to different applications. Some examples are Electrical Tomography (using capacitance, resistance, or impedance), Optical Tomography (using light) and Seismic Tomography (using seismic waves).
A general tomographic system includes one or more sources of the wave or signal used, and one or more detectors tuned to detect variations (for example of intensity) of the same wave or signal. Scanning sources and detectors around the object to image, by a relative movement, or by using more of them, the signal or wave penetrates at least part of the object to be imaged, and thus it is affected in a detectable way (for example, its intensity might decrease). The variations of the signals, along with geometrical information (for example the positions of source and detector), is the data that is fed to the tomographic reconstruction process to obtain an object image (tomogram).
Regardless of the specific wave or signal used, tomographic reconstruction is required to obtain the tomogram from the data gathered. When implemented as computer programs or processes, as is most often the case, tomographic reconstruction processes are expensive in terms of computational resources and memory. In general, the reconstruction process complexity is at least proportional to the size of the input information (sinogram) and to the dimensions of the tomogram. As used herein, sinogram refers to sine-based visual data derived from data collected at detectors. The sinogram may be derived from a geometrical transformation of raw input data. To obtain accurate information on the object, generally it would be desirable to increase both the size of the sinogram and the dimensions of the tomogram, thus increasing the computational and memory loads. Increased computational and memory loads are expensive.
In some applications, the interest does not lie in the whole tomogram. Rather, one might be interested in only specific features, which are currently extracted in a successive feature detection process performed on the tomogram. The cost of this further operation is at least proportional to the dimension of the tomogram. Features of interest may be, for example, particular shapes or intensity profiles on the tomogram. In view of the foregoing, it would be desirable to develop optimized methods for feature detection.